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|
%global _empty_manifest_terminate_build 0
Name: python-qiskit-optimization
Version: 0.5.0
Release: 1
Summary: Qiskit Optimization: A library of quantum computing optimizations
License: Apache-2.0
URL: https://github.com/Qiskit/qiskit-optimization
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c0/a3/9084490d4fbc79ef84e733ccdc706613f248f12252b574f083dacdd9032a/qiskit-optimization-0.5.0.tar.gz
BuildArch: noarch
Requires: python3-qiskit-terra
Requires: python3-scipy
Requires: python3-numpy
Requires: python3-docplex
Requires: python3-setuptools
Requires: python3-networkx
Requires: python3-cplex
Requires: python3-cvxpy
Requires: python3-gurobipy
Requires: python3-matplotlib
%description
### Optional Installs
* **IBM CPLEX** may be installed using `pip install 'qiskit-optimization[cplex]'` to enable the reading of `LP` files and the usage of
the `CplexOptimizer`, wrapper for ``cplex.Cplex``. Currently there is no python 3.9 version of CPLEX. In this case, the CPLEX install
command will have no effect.
* **CVXPY** may be installed using the command `pip install 'qiskit-optimization[cvx]'`.
CVXPY being installed will enable the usage of the Goemans-Williamson algorithm as an optimizer `GoemansWilliamsonOptimizer`.
* **Matplotlib** may be installed using the command `pip install 'qiskit-optimization[matplotlib]'`.
Matplotlib being installed will enable the usage of the `draw` method in the graph optimization application classes.
* **Gurobipy** may be installed using the command `pip install 'qiskit-optimization[gurobi]'`.
Gurobipy being installed will enable the usage of the GurobiOptimizer.
### Creating Your First Optimization Programming Experiment in Qiskit
Now that Qiskit Optimization is installed, it's time to begin working with the optimization module.
Let's try an optimization experiment to compute the solution of a
[Max-Cut](https://en.wikipedia.org/wiki/Maximum_cut). The Max-Cut problem can be formulated as
quadratic program, which can be solved using many several different algorithms in Qiskit.
In this example, the MinimumEigenOptimizer
is employed in combination with the Quantum Approximate Optimization Algorithm (QAOA) as minimum
eigensolver routine.
```python
from docplex.mp.model import Model
from qiskit_optimization.algorithms import MinimumEigenOptimizer
from qiskit_optimization.translators import from_docplex_mp
from qiskit.utils import algorithm_globals
from qiskit.primitives import Sampler
from qiskit.algorithms.minimum_eigensolvers import QAOA
from qiskit.algorithms.optimizers import SPSA
# Generate a graph of 4 nodes
n = 4
edges = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (2, 3, 1.0)] # (node_i, node_j, weight)
# Formulate the problem as a Docplex model
model = Model()
# Create n binary variables
x = model.binary_var_list(n)
# Define the objective function to be maximized
model.maximize(model.sum(w * x[i] * (1 - x[j]) + w * (1 - x[i]) * x[j] for i, j, w in edges))
# Fix node 0 to be 1 to break the symmetry of the max-cut solution
model.add(x[0] == 1)
# Convert the Docplex model into a `QuadraticProgram` object
problem = from_docplex_mp(model)
# Run quantum algorithm QAOA on qasm simulator
seed = 1234
algorithm_globals.random_seed = seed
spsa = SPSA(maxiter=250)
sampler = Sampler()
qaoa = QAOA(sampler=sampler, optimizer=spsa, reps=5)
algorithm = MinimumEigenOptimizer(qaoa)
result = algorithm.solve(problem)
print(result.prettyprint()) # prints solution, x=[1, 0, 1, 0], the cost, fval=4
```
### Further examples
Learning path notebooks may be found in the
[optimization tutorials](https://qiskit.org/documentation/optimization/tutorials/index.html) section
%package -n python3-qiskit-optimization
Summary: Qiskit Optimization: A library of quantum computing optimizations
Provides: python-qiskit-optimization
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-qiskit-optimization
### Optional Installs
* **IBM CPLEX** may be installed using `pip install 'qiskit-optimization[cplex]'` to enable the reading of `LP` files and the usage of
the `CplexOptimizer`, wrapper for ``cplex.Cplex``. Currently there is no python 3.9 version of CPLEX. In this case, the CPLEX install
command will have no effect.
* **CVXPY** may be installed using the command `pip install 'qiskit-optimization[cvx]'`.
CVXPY being installed will enable the usage of the Goemans-Williamson algorithm as an optimizer `GoemansWilliamsonOptimizer`.
* **Matplotlib** may be installed using the command `pip install 'qiskit-optimization[matplotlib]'`.
Matplotlib being installed will enable the usage of the `draw` method in the graph optimization application classes.
* **Gurobipy** may be installed using the command `pip install 'qiskit-optimization[gurobi]'`.
Gurobipy being installed will enable the usage of the GurobiOptimizer.
### Creating Your First Optimization Programming Experiment in Qiskit
Now that Qiskit Optimization is installed, it's time to begin working with the optimization module.
Let's try an optimization experiment to compute the solution of a
[Max-Cut](https://en.wikipedia.org/wiki/Maximum_cut). The Max-Cut problem can be formulated as
quadratic program, which can be solved using many several different algorithms in Qiskit.
In this example, the MinimumEigenOptimizer
is employed in combination with the Quantum Approximate Optimization Algorithm (QAOA) as minimum
eigensolver routine.
```python
from docplex.mp.model import Model
from qiskit_optimization.algorithms import MinimumEigenOptimizer
from qiskit_optimization.translators import from_docplex_mp
from qiskit.utils import algorithm_globals
from qiskit.primitives import Sampler
from qiskit.algorithms.minimum_eigensolvers import QAOA
from qiskit.algorithms.optimizers import SPSA
# Generate a graph of 4 nodes
n = 4
edges = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (2, 3, 1.0)] # (node_i, node_j, weight)
# Formulate the problem as a Docplex model
model = Model()
# Create n binary variables
x = model.binary_var_list(n)
# Define the objective function to be maximized
model.maximize(model.sum(w * x[i] * (1 - x[j]) + w * (1 - x[i]) * x[j] for i, j, w in edges))
# Fix node 0 to be 1 to break the symmetry of the max-cut solution
model.add(x[0] == 1)
# Convert the Docplex model into a `QuadraticProgram` object
problem = from_docplex_mp(model)
# Run quantum algorithm QAOA on qasm simulator
seed = 1234
algorithm_globals.random_seed = seed
spsa = SPSA(maxiter=250)
sampler = Sampler()
qaoa = QAOA(sampler=sampler, optimizer=spsa, reps=5)
algorithm = MinimumEigenOptimizer(qaoa)
result = algorithm.solve(problem)
print(result.prettyprint()) # prints solution, x=[1, 0, 1, 0], the cost, fval=4
```
### Further examples
Learning path notebooks may be found in the
[optimization tutorials](https://qiskit.org/documentation/optimization/tutorials/index.html) section
%package help
Summary: Development documents and examples for qiskit-optimization
Provides: python3-qiskit-optimization-doc
%description help
### Optional Installs
* **IBM CPLEX** may be installed using `pip install 'qiskit-optimization[cplex]'` to enable the reading of `LP` files and the usage of
the `CplexOptimizer`, wrapper for ``cplex.Cplex``. Currently there is no python 3.9 version of CPLEX. In this case, the CPLEX install
command will have no effect.
* **CVXPY** may be installed using the command `pip install 'qiskit-optimization[cvx]'`.
CVXPY being installed will enable the usage of the Goemans-Williamson algorithm as an optimizer `GoemansWilliamsonOptimizer`.
* **Matplotlib** may be installed using the command `pip install 'qiskit-optimization[matplotlib]'`.
Matplotlib being installed will enable the usage of the `draw` method in the graph optimization application classes.
* **Gurobipy** may be installed using the command `pip install 'qiskit-optimization[gurobi]'`.
Gurobipy being installed will enable the usage of the GurobiOptimizer.
### Creating Your First Optimization Programming Experiment in Qiskit
Now that Qiskit Optimization is installed, it's time to begin working with the optimization module.
Let's try an optimization experiment to compute the solution of a
[Max-Cut](https://en.wikipedia.org/wiki/Maximum_cut). The Max-Cut problem can be formulated as
quadratic program, which can be solved using many several different algorithms in Qiskit.
In this example, the MinimumEigenOptimizer
is employed in combination with the Quantum Approximate Optimization Algorithm (QAOA) as minimum
eigensolver routine.
```python
from docplex.mp.model import Model
from qiskit_optimization.algorithms import MinimumEigenOptimizer
from qiskit_optimization.translators import from_docplex_mp
from qiskit.utils import algorithm_globals
from qiskit.primitives import Sampler
from qiskit.algorithms.minimum_eigensolvers import QAOA
from qiskit.algorithms.optimizers import SPSA
# Generate a graph of 4 nodes
n = 4
edges = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (2, 3, 1.0)] # (node_i, node_j, weight)
# Formulate the problem as a Docplex model
model = Model()
# Create n binary variables
x = model.binary_var_list(n)
# Define the objective function to be maximized
model.maximize(model.sum(w * x[i] * (1 - x[j]) + w * (1 - x[i]) * x[j] for i, j, w in edges))
# Fix node 0 to be 1 to break the symmetry of the max-cut solution
model.add(x[0] == 1)
# Convert the Docplex model into a `QuadraticProgram` object
problem = from_docplex_mp(model)
# Run quantum algorithm QAOA on qasm simulator
seed = 1234
algorithm_globals.random_seed = seed
spsa = SPSA(maxiter=250)
sampler = Sampler()
qaoa = QAOA(sampler=sampler, optimizer=spsa, reps=5)
algorithm = MinimumEigenOptimizer(qaoa)
result = algorithm.solve(problem)
print(result.prettyprint()) # prints solution, x=[1, 0, 1, 0], the cost, fval=4
```
### Further examples
Learning path notebooks may be found in the
[optimization tutorials](https://qiskit.org/documentation/optimization/tutorials/index.html) section
%prep
%autosetup -n qiskit-optimization-0.5.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-qiskit-optimization -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.0-1
- Package Spec generated
|
